Towards a relation extractor U-shaped network for accurate pulmonary vessel segmentation in CT images

Abstract

Pulmonary vessel segmentation from CT images is essential to diagnosis and treatment of lung diseases, particularly in treatment planning and clinical outcome evaluation. The main challenge for pulmonary vessel segmentation is complicated structures of the vascular trees and their similar intensity values with other tissues like the tracheal wall and lung nodules. This paper presents a novel relation extractor U-shaped network combining convolution and self-attention mechanism in an encoder-decoder mode. Particularly, we employ convolution in the shallow layers to extract local information of vessels in a short range and apply self-attention in the deep layers to capture long-range contextual relationship between ancestors and descendants of the vascular tree. We evaluate our proposed method on 50 computer tomography volumes, with the experimental results showing that our method can improve the average coefficient dice and recall to 85.60 and 86.04 respectively.